A generalized data-driven speech enhancement framework for bilateral cochlear implants

This paper examines environment-adaptive noise suppression algorithms for computationally efficient or real-time implementation in bilateral cochlear implants using a single processor. A generalized framework is introduced that allows one to train suppression and head-related transfer function gain tables not only for different noise environments but also for different distortion measures. This generalization incorporates any differentiable measure with unilateral data-driven enhancement methods becoming its special cases. Specifically, the solutions for three distortion measures of Weighted-Euclidean, Log-Euclidean and Weighted-Cosh are provided. These solutions are evaluated in six commonly encountered noise environments for a wide range of directionalities.